4B.004 Time series spatial analysis framework to identify prominent crash locations: Case study Delhi. (14th March 2021)

• Record Type: Journal Article
• Authors: Ahuja, Richa; Bisht, Laxman; Tiwari, Geetam
• Is Part Of: Injury prevention. Volume 27(2021)Supplement 2
• Page Start: A34
• Page End: A34
• Publication Date: 2021-03-14
• Subjects: Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/injuryprev-2021-safety.103
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Introduction: Road traffic crashes(RTC's) form unresolved burden on developmental goals of most Low and Middle Income countries. Enabling safer road environment by introducing traffic calming and traffic management is mostly resorted strategy to reduce RTC's. Random spatial occurrence of these crashes, unavailability of trained manpower for assessment and financial constraints makes it difficult for planners to prioritize locations for remedial investments. Aim: Study aims to demonstrate a framework to identify and prioritize prominent crash locations for further field evaluation and remediation. Data and Methodology: Road traffic crash location data is collected for 11 years (2006 to 2016) for crashes with at least 1 fatality in Delhi from First Information Reports. This time series data is segregated. Independent and cumulative hotspot analysis is performed for all 11 years. Spatial analysis toolbox is used to identify most prominent crash hotspots and assigning them priority order for further on-field investigation. Results: Study identifies 17, 779 fatal crashes in 11 years. 11 year individual hotspot analysis forms total 1, 451 hotspot locations requiring remedial action. 714 locations have been consistent hotspots for 11 years, with 80 of these being hotspots for 3 or more years. Conclusion: Focused investment solutions in road safety can be developed based on minimal spatial data. Study identifies stagnant hotspots in Delhi over 11 years. The methodology developed will be useful for policy makers to prioritize actions for maximum reduction in crashes. Learning Outcomes: Study provides spatial framework to identify and prioritize actionable locations using limited, generally available data for reducing road traffic crash incidents.